Baselines.
We consider the following baselines:
1) Static reconstruction: we adopt state-of-the-art methods ONet [1]} and PIFuHD [2]}, and train them for per-frame static reconstruction. For a fair comparison, we train these two networks in a class-agnostic setting using all the frames in the training videos.
2) Fusion-based dynamic reconstruction: most fusion based methods [3]}, [4]}, [5]} are neither open-sourced nor reproduced. Among the available ones, we adapt the author-released SurfelWarp [6]} due to its superior performance.
Since this method is non-parametric and requires no training, we directly apply it on the validation/testing clips.
3) Supervised dynamic reconstruction: REDO learns to reconstruct dynamic objects in a supervised manner. OFlow [7]} also falls into this category but it doesn't handle the partial observation and rigid-motion.
Note, REDO uses each clip's center frame as the canonical space while OFlow uses the initial one.
Therefore, for a fair comparison, we set OFlow's first frame to be the center of our input clip.
